- Point estimation
In
statistics , point estimation involves the use of sampledata to calculate a single value (known as astatistic ) which is to serve as a "best guess" for an unknown (fixed or random) populationparameter .More formally, it is the application of a point
estimator to the data.In general, point estimation should be contrasted with
interval estimation .Point estimation should be contrasted with general Bayesian methods of estimation, where the goal is usually to compute (perhaps to an approximation) the
posterior distribution s of parameters and other quantities of interest. The contrast here is between estimating a single point (point estimation), versus estimating a weighted set of points (aprobability density function ). However, where appropriate, Bayesian methodology can include the calculation of point estimates, either as the expectation or median of the posterior distribution or as the mode of this distribution.In a purely
frequentist context (as opposed to Bayesian), point estimation should be contrasted with the calculation ofconfidence intervals .Routes to deriving point estimates directly
*
maximum likelihood (ML)
*method of moments,generalized method of moments
*minimum mean squared error (MMSE)
*minimum variance unbiased estimator (MVUE)
*best linear unbiased estimator (BLUE)Routes to deriving point estimates via Bayesian Analysis
*
maximum a posteriori (MAP)
*particle filter
*Markov chain Monte Carlo (MCMC)
*Kalman filter
*Wiener filter Properties of Point estimates
*
bias of an estimator
*Cramér-Rao bound
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